Triple
T35387361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarasaland |
E1022824
|
entity |
| Predicate | antagonistInvasion |
P89676
|
FINISHED |
| Object | Tatanga |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tatanga | Statement: [Sarasaland, antagonistInvasion, Tatanga]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: antagonistInvasion Context triple: [Sarasaland, antagonistInvasion, Tatanga]
-
A.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
B.
antagonistActionOf
Indicates that one entity performs an action in opposition or hostility toward another entity, acting as its antagonist.
-
C.
antagonistInvolved
chosen
Indicates that an antagonist participates in, influences, or is otherwise actively involved in the referenced event or situation.
-
D.
antagonistNearby
Indicates that an opposing or hostile entity is located in close physical proximity to the reference entity.
-
E.
hasAntagonisticProtagonist
Indicates that the work features a main character who opposes or undermines the typical heroic or moral expectations of a traditional protagonist.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76df34ba48190bd80f0814cdcd540 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:03 p.m.